Quantification of similarity and physical awareness of microstructures generated via generative models

Thakre, Sanket; Karan, Vir; Kanjarla, Anand K.

doi:10.1016/j.commatsci.2023.112074

Cited by 11 publications

(3 citation statements)

References 62 publications

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“…Generative models such as GANs, which are popular in image recognition, are being increasingly used to handle the materials images such as microstructures. The methods can effectively learn the information encoded in the images and then generate new images [77–81] . In addition, one can relate the learned representation to property of interest.…”

Section: Application Of Gans In Materials Microstructurementioning

confidence: 99%

Applications of generative adversarial networks in materials science

Jiang,

Li,

Yang

et al. 2024

Materials Genome Engineering Advances

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Generative adversarial networks (GANs), as a powerful tool for inverse materials discovery, are being increasingly applied in various fields of materials science. This review provides systematic investigations on the applications of GANs from a group of different aspects. The basic principles of GANs are first introduced; then a detailed review of GANs‐based studies regarding distinct scenarios across composition design, processing optimization, crystal structure search, microstructure characterization and defect detection is presented. At the end, several challenges and possible solutions are discussed and outlined. This overview highlights the efficacy of GANs in materials science, and may stimulate the further use of GANs for more intriguing achievements.

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Section: Application Of Gans In Materials Microstructurementioning

confidence: 99%

Applications of generative adversarial networks in materials science

Jiang,

Li,

Yang

et al. 2024

Materials Genome Engineering Advances

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“…Tharke et al [175] quantified the physical awareness of dual-phase steel microstructures using a variant of GAN. The similarity between the original and the generated microstructures was quantified on the basis of signal-to-noise ratio, similarity index, and peak signal-to-noise ratio.…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Kibrete,

Trzepieciński,

Gebremedhen

et al. 2023

J. Compos. Sci.

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The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.

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“…The first class consists of mathematical measures such as the widely used mean squared error (MSE) [1], peak signal to noise ratio (PSNR) [2], root mean squared error (RMSE) [3], mean absolute error (MAE) [4], and signal-to-noise ratio (SNR) [5], which have been used because of their simplicity in calculation and having low computational complexity [2].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

Farhadinia,

Ahangari,

Heydari

2024

Mathematics

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Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. One important aspect of image processing is the ability to compare and measure the similarity between different images by providing a way to quantify the similarity between images using various features such as contrast, luminance, and structure. Generally, the flexibility of similarity measures enables fine-tuning the comparison process to achieve the desired outcomes. This is while the existing similarity measures are not flexible enough to address diverse and comprehensive practical aspects. To this end, we utilize triangular norms (t-norms) to construct an inclusive class of similarity measures in this article. As is well-known, each t-norm possesses distinctive attributes that allow for novel interpretations of image similarities. The proposed class of t-norm-based structural similarity measures offers numerous options for decisionmakers to consider various issues and interpret results more broadly in line with their objectives. For more details, in the Experiments section, the proposed method is applied to grayscale and binarized images and a specific experiment related to meteorology. Eventually, the presented diverse case studies confirm the efficiency and key features of the t-norm-based structural similarity.

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Quantification of similarity and physical awareness of microstructures generated via generative models

Cited by 11 publications

References 62 publications

Applications of generative adversarial networks in materials science

Applications of generative adversarial networks in materials science

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology

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